Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling

Researchers have proposed a novel method called Context-Aligned Contrastive Regression to enhance lexical difficulty prediction, a critical task in language learning and readability assessment. Existing models often rely on regression-only training with scalar supervision, which fails to explicitly structure the representation space, thereby limiting cross-lingual alignment and ordinal difficulty capture. To address these limitations, the new approach integrates a Ridge regression ensemble with two complementary objectives: Cross-View Context and Ordinal Soft Contrastive Learning. Experiments conducted on three first-language (L1) datasets demonstrate significant improvements. The contrastive objectives successfully improve cross-lingual representation alignment while preserving language-specific nuances. Furthermore, the learned representations effectively capture the ordinal structure of lexical difficulty. The ensemble mechanism also mitigates systematic biases found in individual models, resulting in more stable performance across various difficulty levels. This study, published on arXiv under computer science categories for Computation and Language and Artificial Intelligence, offers a robust solution for estimating word difficulty across different linguistic backgrounds.